Author

Jianwei Zhu

Microsoft Research Asia (China)ORCID 0000-0002-8272-9190Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2 Genetics· 1 Molecular Biology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 4 Assay:protein sequence· 3 Research Focus:protein structure prediction· 3 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Assay:ChIP-seq· 1 Research Focus:differential expression· 1 Approach:diffusion model· 1

Frequent Co-Authors

Fusong JuiD· 4 shared Bin ShaoiD· 2 shared Dongbo BuiD· 2 shared Huanhuan XiaiD· 2 shared Lupeng KongiD· 2 shared Wei‐Mou ZhengiD· 2 shared Chang LiuiD· 1 shared Chenao LiiD· 1 shared

Activity by Year

2021-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 26 mutual co-author links

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4 shared papersBioinformatics

Fusong JuORCID

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Author profile →

4 papers

Recent·Most cited

FlexRibbon: Joint Sequence and Structure Pretraining for Protein Modeling

Jianwei Zhu, Yu Shi, Ran Bi, Peiran Jin, Chang Liu, Zhenyu Zhang, Haitao Huang, Zekun Guo, Pipi Hu, Fusong Ju, Lin Huang, Xinwei Tai, Chenao Li, Kaiyuan Gao, Xinran Wei, , Tao Qin

FlexRibbon is a pretrained protein model that jointly learns from sequences and structures, achieving state-of-the-art performance in mutation-rich settings.

bioRxiv · Oct 8, 2025 · PreprintMolecular Biology

CREaTor: zero-shot cis-regulatory pattern modeling with attention mechanisms

Yongge Li, Fusong Ju, Zhiyuan Chen, Yiming Qu, Huanhuan Xia, Liang He, Lijun Wu, Jianwei Zhu, Bin Shao, Pan Deng

CREaTor models cis-regulatory patterns using attention mechanisms and RNA-seq/ChIP-seq data, outperforming existing methods in predicting cCRE-gene interactions across cell types.

Genome biology · Nov 23, 2023Peer-reviewedGenetics

ProALIGN: Directly learning alignments for protein structure prediction via exploiting context-specific alignment motifs

Lupeng Kong, Fusong Ju, Wei‐Mou Zheng, Jianwei Zhu, Shiwei Sun, Jinbo Xu, Dongbo Bu

ProALIGN improves protein structure prediction by learning optimal sequence-template alignments using deep learning, outperforming existing methods on multiple datasets.

Journal of Computational Biology · Jan 24, 2022Peer-reviewedBioinformatics

CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction

Fusong Ju, Jianwei Zhu, Bin Shao, Lupeng Kong, Tie‐Yan Liu, Wei‐Mou Zheng, Dongbo Bu

CopulaNet directly learns residue co-evolution from multiple sequence alignments to improve protein structure prediction accuracy and efficiency.

Nature Communications · May 5, 2021Peer-reviewedBioinformatics